Optimal Ordered Problem Solver
暂无分享,去创建一个
[1] K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .
[2] K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .
[3] A. Church. Review: A. M. Turing, On Computable Numbers, with an Application to the Entscheidungsproblem , 1937 .
[4] A. Turing. On computable numbers, with an application to the Entscheidungsproblem , 1937, Proc. London Math. Soc..
[5] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[6] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..
[7] John von Neumann,et al. Theory Of Self Reproducing Automata , 1967 .
[8] A. Kolmogorov. Three approaches to the quantitative definition of information , 1968 .
[9] C. Cordell Green,et al. Application of Theorem Proving to Problem Solving , 1969, IJCAI.
[10] Richard C. T. Lee,et al. PROW: A Step Toward Automatic Program Writing , 1969, IJCAI.
[11] Charles H. Moore,et al. Forth - a language for interactive computing , 1970 .
[12] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[13] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[14] W. Vent,et al. Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .
[15] G. Chaitin. A Theory of Program Size Formally Identical to Information Theory , 1975, JACM.
[16] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[17] David Bulman. Stack Computers [Guest editor's introduction] , 1977, Computer.
[18] Michiharu Tsukamoto. Program Stacking Technique , 1977 .
[19] Hans J. Bremermann,et al. Minimum energy requirements of information transfer and computing , 1982 .
[20] Charles H. Bennett,et al. The thermodynamics of computation—a review , 1982 .
[21] T. Toffoli,et al. Conservative logic , 2002, Collision-Based Computing.
[22] Douglas B. Lenat,et al. Theory Formation by Heuristic Search , 1983, Artificial Intelligence.
[23] Stephen Wolfram,et al. Universality and complexity in cellular automata , 1983 .
[24] Leonid A. Levin,et al. Randomness Conservation Inequalities; Information and Independence in Mathematical Theories , 1984, Inf. Control..
[25] Paul E. Utgoff,et al. Shift of bias for inductive concept learning , 1984 .
[26] Nichael Lynn Cramer,et al. A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.
[27] Pat Langley,et al. Learning to Search: From Weak Methods to Domain-Specific Heuristics , 1985, Cogn. Sci..
[28] Pat Langley,et al. Learning to search : from weak methods to domain-specific heuristics , 1985 .
[29] John H. Holland,et al. Properties of the Bucket Brigade , 1985, ICGA.
[30] Ray J. Solomonoff,et al. The Application of Algorithmic Probability to Problems in Artificial Intelligence , 1985, UAI.
[31] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[32] Charles W. Anderson,et al. Learning and problem-solving with multilayer connectionist systems (adaptive, strategy learning, neural networks, reinforcement learning) , 1986 .
[33] Allen Newell,et al. GPS, a program that simulates human thought , 1995 .
[34] B. Widrow,et al. The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.
[35] Jr. Philip J. Koopman,et al. Stack computers: the new wave , 1989 .
[36] R. Solomonoff. A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY , 1989 .
[37] Ray J. Solomonofi,et al. A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY , 1989 .
[38] Jürgen Schmidhuber,et al. Reinforcement Learning in Markovian and Non-Markovian Environments , 1990, NIPS.
[39] Konrad Zuse,et al. Rechnender Raum , 1991, Physik und Informatik.
[40] Vladimir Vapnik,et al. Principles of Risk Minimization for Learning Theory , 1991, NIPS.
[41] Michael I. Jordan,et al. Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..
[42] Jürgen Schmidhuber,et al. A ‘Self-Referential’ Weight Matrix , 1993 .
[43] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[44] J. Schmidhuber. An 'introspective' network that can learn to run its own weight change algorithm , 1993 .
[45] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[46] Yves Deville,et al. Logic Program Synthesis , 1994, J. Log. Program..
[47] Juergen Schmidhuber,et al. On learning how to learn learning strategies , 1994 .
[48] Corso Elvezia. Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability , 1995 .
[49] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[50] Roland Olsson,et al. Inductive Functional Programming Using Incremental Program Transformation , 1995, Artif. Intell..
[51] D. Wolpert,et al. No Free Lunch Theorems for Search , 1995 .
[52] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[53] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[54] Jürgen Schmidhuber,et al. Solving POMDPs with Levin Search and EIRA , 1996, ICML.
[55] Jieyu Zhao,et al. Simple Principles of Metalearning , 1996 .
[56] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[57] Jürgen Schmidhuber,et al. Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability , 1997, Neural Networks.
[58] William I. Gasarch,et al. Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.
[59] Rafal Salustowicz,et al. Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.
[60] Bernhard Nebel,et al. Extending Planning Graphs to an ADL Subset , 1997, ECP.
[61] Jürgen Schmidhuber,et al. Reinforcement Learning with Self-Modifying Policies , 1998, Learning to Learn.
[62] Jürgen Schmidhuber,et al. Evolving Structured Programs with Hierarchical Instructions and Skip Nodes , 1998, ICML.
[63] Peter Nordin,et al. Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .
[64] Luca Maria Gambardella,et al. Ant Algorithms for Discrete Optimization , 1999, Artificial Life.
[65] Luca Maria Gambardella,et al. An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem , 2000, INFORMS J. Comput..
[66] S. Lloyd. Ultimate physical limits to computation , 1999, Nature.
[67] Jürgen Schmidhuber,et al. Algorithmic Theories of Everything , 2000, ArXiv.
[68] Sepp Hochreiter,et al. Learning to Learn Using Gradient Descent , 2001, ICANN.
[69] Jürgen Schmidhuber,et al. Market-Based Reinforcement Learning in Partially Observable Worlds , 2001, ICANN.
[70] Marcus Hutter,et al. Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions , 2000, ECML.
[71] Jürgen Schmidhuber,et al. Sequential Decision Making Based on Direct Search , 2001, Sequence Learning.
[72] Ofi rNw8x'pyzm,et al. The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002 .
[73] Jürgen Schmidhuber,et al. Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit , 2002, Int. J. Found. Comput. Sci..
[74] Jürgen Schmidhuber,et al. Bias-Optimal Incremental Problem Solving , 2002, NIPS.
[75] Marcus Hutter. The Fastest and Shortest Algorithm for all Well-Defined Problems , 2002, Int. J. Found. Comput. Sci..
[76] Marcus Hutter,et al. Self-Optimizing and Pareto-Optimal Policies in General Environments based on Bayes-Mixtures , 2002, COLT.
[77] Jürgen Schmidhuber,et al. Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements , 2003, ArXiv.
[78] Jürgen Schmidhuber,et al. Exploring the predictable , 2003 .
[79] Schmidhuber Juergen,et al. The New AI: General & Sound & Relevant for Physics , 2003 .
[80] Eric B. Baum,et al. Toward a Model of Intelligence as an Economy of Agents , 1999, Machine Learning.
[81] Andrea Bonarini,et al. Bias-Optimal Incremental Learning of Control Sequences for Virtual Robots , 2004 .
[82] Jürgen Schmidhuber,et al. Learning Team Strategies: Soccer Case Studies , 1998, Machine Learning.
[83] Jürgen Schmidhuber,et al. Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.